Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	129,662
2	South Dakota	125,012
3	Rhode Island	115,231
4	Utah	113,023
5	Arizona	109,901
6	Tennessee	109,264
7	Oklahoma	104,822
8	Iowa	104,694
9	Wisconsin	104,495
10	Arkansas	103,891
11	Nebraska	102,307
12	Kansas	99,733
13	Alabama	98,402
14	Indiana	97,102
15	Mississippi	97,009
16	South Carolina	95,257
17	Idaho	94,063
18	Nevada	93,861
19	Wyoming	92,181
20	Illinois	92,164
21	Montana	91,468
22	Louisiana	90,430
23	Georgia	89,036
24	Texas	88,820
25	California	88,548
26	Kentucky	88,374
27	New Mexico	86,349
28	Delaware	85,615
29	Florida	85,543
30	New Jersey	84,558
31	Minnesota	84,170
32	Missouri	83,039
33	Massachusetts	81,331
34	Ohio	80,698
35	New York	79,850
36	North Carolina	79,243
37	Alaska	77,239
38	Connecticut	75,960
39	Colorado	72,531
40	West Virginia	71,487
41	Pennsylvania	70,629
42	Virginia	64,824
43	Michigan	63,222
44	Maryland	61,598
45	District of Columbia	55,446
46	New Hampshire	52,375
47	Washington	43,754
48	Puerto Rico	41,310
49	Oregon	35,771
50	Maine	31,816
51	Vermont	22,303
52	Hawaii	18,941

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Carolina	473
2	Rhode Island	393
3	New York	369
4	Connecticut	325
5	New Jersey	271
6	Mississippi	265
7	North Carolina	264
8	Kentucky	252
9	Delaware	238
10	Florida	238
11	Virginia	229
12	Alaska	226
13	Massachusetts	226
14	Tennessee	213
15	Georgia	212
16	Oklahoma	210
17	Vermont	190
18	Pennsylvania	186
19	Utah	183
20	Alabama	178
21	Arizona	176
22	New Hampshire	174
23	California	166
24	Ohio	163
25	District of Columbia	158
26	West Virginia	155
27	Kansas	154
28	Wyoming	152
29	South Dakota	146
30	Indiana	142
31	Colorado	140
32	Iowa	135
33	Nevada	132
34	Montana	131
35	Louisiana	129
36	Texas	127
37	Illinois	122
38	New Mexico	122
39	Maryland	115
40	Minnesota	108
41	Arkansas	106
42	Puerto Rico	104
43	Wisconsin	101
44	North Dakota	100
45	Idaho	99
46	Washington	94
47	Maine	86
48	Michigan	82
49	Nebraska	78
50	Missouri	77
51	Oregon	66
52	Hawaii	21

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,532
2	New York	2,348
3	Massachusetts	2,258
4	Rhode Island	2,212
5	Mississippi	2,184
6	Connecticut	2,089
7	South Dakota	2,084
8	Arizona	2,058
9	Louisiana	2,005
10	North Dakota	1,917
11	Alabama	1,887
12	Indiana	1,816
13	Pennsylvania	1,809
14	Arkansas	1,751
15	Illinois	1,751
16	New Mexico	1,693
17	Iowa	1,668
18	Michigan	1,615
19	Tennessee	1,590
20	South Carolina	1,564
21	Nevada	1,537
22	Kansas	1,512
23	Georgia	1,477
24	Texas	1,432
25	Ohio	1,407
26	District of Columbia	1,391
27	Florida	1,357
28	Delaware	1,325
29	Missouri	1,285
30	Maryland	1,259
31	Montana	1,246
32	West Virginia	1,236
33	California	1,200
34	Wisconsin	1,166
35	Minnesota	1,143
36	Wyoming	1,143
37	Nebraska	1,100
38	Colorado	1,030
39	Oklahoma	1,026
40	Idaho	1,011
41	North Carolina	1,011
42	Kentucky	1,005
43	New Hampshire	835
44	Virginia	824
45	Washington	627
46	Puerto Rico	602
47	Utah	560
48	Oregon	506
49	Maine	484
50	Alaska	377
51	Vermont	306
52	Hawaii	298

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Rhode Island	16
2	Georgia	9
3	South Carolina	9
4	Wyoming	8
5	Florida	7
6	Connecticut	6
7	Massachusetts	6
8	New York	6
9	New Mexico	5
10	Oklahoma	5
11	California	4
12	Indiana	4
13	Kansas	4
14	Maryland	4
15	Mississippi	4
16	Nevada	4
17	North Carolina	4
18	Arkansas	3
19	Delaware	3
20	Kentucky	3
21	Louisiana	3
22	Ohio	3
23	Alaska	2
24	Illinois	2
25	Iowa	2
26	New Jersey	2
27	South Dakota	2
28	Tennessee	2
29	Texas	2
30	West Virginia	2
31	Wisconsin	2
32	Alabama	1
33	Arizona	1
34	Colorado	1
35	District of Columbia	1
36	Michigan	1
37	Montana	1
38	New Hampshire	1
39	Pennsylvania	1
40	Vermont	1
41	Virginia	1
42	Washington	1
43	Hawaii	0
44	Idaho	0
45	Maine	0
46	Minnesota	0
47	Missouri	0
48	Nebraska	0
49	North Dakota	0
50	Oregon	0
51	Puerto Rico	0
52	Utah	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	318,594	1	99
Chattahoochee	Georgia	269,185	2	99
Bent	Colorado	255,334	3	99
Lincoln	Arkansas	239,788	4	99
Lake	Tennessee	237,600	5	99
Davidson	Tennessee	127,100	225	92
Richland	South Carolina	94,038	1210	61
York	South Carolina	87,793	1525	51
Orange	California	81,113	1839	41
Pierce	Washington	41,099	2900	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Galax city	Virginia	6,775	3	99
Iron	Wisconsin	6,682	4	99
Buffalo	South Dakota	6,626	5	99
Davidson	Tennessee	1,173	1926	38
Orange	California	1,139	1964	37
Richland	South Carolina	1,138	1966	37
York	South Carolina	1,068	2067	34
Pierce	Washington	564	2684	14

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.